Upon control of a controlled system by a controller, it is necessary to tune one or more control parameters of the controller in the light of the characteristics of the controlled system. As one method therefor, there is the tuning method described in "Fuzzy Suiron o Oyoshita PID Controller yo Autotuning Hoshiki (Autotuning Method for PID Controller, Using Fuzzy Inference)", the Thirteenth Symposium of the Measurement and Automatic Control Society, November 1987. The outline of this conventional tuning method will hereinafter be described.
FIG. 16 is a block diagram showing the construction of an autotuner for realizing the conventional tuning method.
The combined controlling-controlled system shown in FIG. 16 comprises a controlled system 101, a PID controller 102 for sending a manipulated variable to the controlled system, an adder 103 for calculating the difference between a controlled variable y of the controlled system 101 and a desired value r, and an autotuner 104 for performing tuning of a control parameter of the PID controller 102.
The autotuner 104 in turn comprises a characteristic quantity extraction unit 105 for extracting a characteristic quantity with respect to each of the desired value r and the controlled variable y, a tuning rule storage unit 106 for storing, as a qualitative tuning rule, empirical knowledge and know-how of skilled operators on the tuning of the control parameter, and a fuzzy inference unit 107 for determining the control parameter for the thus-extracted characteristic quantity by using fuzzy inference on the basis of the tuning rule.
Such conventional tuning of a parameter by an autotuner is carried out by extracting characteristic quantities such as overshoot E, damping ratio D and oscillation period ratio R, which are shown in FIG. 17, from a response waveform of the controlled variable when the desired value is changed stepwise and then determining a control parameter by fuzzy inference on the basis of these characteristic quantities.
According to fuzzy inference, a tuning rule which is qualitatively expressed, for example, as will be shown next is defined by a fuzzy rule and a control parameter is then determined from characteristic quantities by fuzzy computation while using the fuzzy rule.
If the overshoot E and the damping ratio D are large, proportional gain K.sub.p and derivative time T.sub.d are set smaller.
The control parameter which has been obtained as described above is fed to the PID controller 102, whereby the controller 102 performs control on the basis of the parameter so supplied.
In the above-described conventional autotuner, as an element or requirement for appropriately performing the tuning of the parameter, development of a tuning rule is mentioned. This tuning rule prepares IF-THEN type rules for various variations of a process. Several tens or more of fuzzy rules have heretofore been required, with a further requirement that they should not involve contradiction as a whole. Moreover, a major portion of the preparation of such fuzzy rules has to be performed by man power.
Accordingly, the above-described conventional technique involves the problem that a lot of time is required for the development of fuzzy rules for tuning, in other words, of tuning rules.
The above-described conventional technique uses, as characteristic quantities of the process, characteristic quantities of a response waveform of the controlled variable y for stepwise changes of the desired value r. The characteristic quantities of the process are prerequisite for fuzzy inference. An operation in which a desired value is not changed stepwise is however accompanied by the problem that tuning of a controller is difficult, because extraction of such characteristic quantities are infeasible.
Reference may also be had, as a related prior art publication, to U.S. Pat. No. 4,602,326 entitled "Pattern-Recognition Self-Tuning Controller", although no fuzzy inference is used therein.
As another method for tuning a control parameter of a controller in accordance with characteristics of a controlled system, there is the tuning method described in "Tekio Seigyo System no Riron to Jissai (Theory and Practice of Adaptive Control Systems)" coauthored by I. D. Landau and Masayoshi Tomizuka, The Ohmsha Co., Ltd. (published Dec. 10, 1982). The outline of this conventional tuning method will hereinafter be described.
FIG. 30 is a block diagram showing the details of the conventional tuning method. According to this method, a controller 108 is tuned such that characteristics of a combined controlling-controlled system, namely, of a system composed in combination of the controller 108 and the controlled system 101 can be brought into conformity with characteristics of a reference model 109 with desired response characteristics. This tuning method is therefore called the "model-reference-type adaptive control method". In this method, a tuning algorithm for the controller can be given by a formula when a controlled system is a linear system, in other words, has linear characteristics. Formation of such a formula has however been difficult when a controlled system has non-linear characteristics.
To control a plant, it is necessary to understand characteristics of the plant and then to determine an optimal manipulated variable on the basis of the characteristics. As a method for this purpose, there has been the prediction adaptive control method for plants, which is described in (1) "Steam Temperature Prediction Control for Thermal Power Plant", IEEE, Trans. on Power Apparatus and Systems, PAS-103 (9), 2382-2387, September 1984 or (2) "Adaptive Optimal Control of Steam Temperatures for Thermal Power Plants", IEEE, Trans. on Energy Conversion, 4(1), 25-33, March 1989. The prediction adaptive control method for plants is applied to a thermal power plant in these publications. Namely, a model of a thermal power plant is built in a control system, parameters of the model are identified based on operation data of the plant, operation of the plant in the near future is predicted using the thus-identified model, and a manipulated variable is then determined based on the results of the prediction.
Incidentally, characteristics of a thermal power plant are expressed by a non-linear differential equation as shown by the following equation: ##EQU1## where x.sub.i : state variable representing temperature, pressure or the like,
u.sub.i : manipulated variable indicating fuel quantity, feedwater quantity or the like, and PA1 f.sub.i : non-linear function.
As a model for a thermal power plant defined by equation (80), (i) a physical model and (ii) a linear regression model have conventionally be used. The physical model (i) makes use of a mathematical formula representing physical phenomena such as mass balance, energy balance and momentum balance and simulates a part of the characteristics of the thermal power plant represented by the equation (80). A feature of such a physical model resides in that it permits simulation of non-linear characteristics which vary depending on the load level. It is however difficult to realize a large and complex physical model because of the difficulties in tuning model parameters.
The linear regression model (ii) has simulated a part of characteristics of a thermal power plant, which is represented by equation (80), by using a mathematical formula which estimates an output variable of the thermal power plant by linear connection of time-series signals of an input/output variable of the thermal power plant. A feature of the linear regression model resides in that a relatively large and complex model can be realized. It is however difficult for the linear regression model to simulate a non-linear characteristic which varies depending on the load level.
Regarding the conventional techniques described above, it is difficult to realize, with the physical model (i), a model capable of simulating the entirety thermal power plant. There is hence no choice other than using a model which can simulate only a part of a thermal power plant, such as a secondary superheater. This inherently poses a limitation on the prediction of the behavior or state of a thermal power plant, which is a multi-input/multi-output system, in the near future, thereby also posing a limitation on the improvement of controllability. The linear regression model (ii) permits simulation of most of a thermal power plant. It is however necessary to identify a model parameter responsive to each change in load because the thermal power plant has non-linear characteristics that varies depending on the load level. The speed of the identification cannot follow changes in load when the changes in load take place at high speed, resulting in the problem that a limitation is imposed on the improvement of controllability.